Machine Learning Hyperparameter Fine Tuning Service on Dynamic Cloud Resource Allocation System - taking Heart Sounds as an Example

Abstract

The hyperparameters tuning of machine learning has always been a difficult and time-consuming task in deep learning area. In many practical applications, the hyperparameter tuning directly affects the accuracy. Therefore, the tuning optimization of hyperparameters is an important topic. At present, hyperparameters can only be set manually based on experience, and use Violent Enumeration, Random Search or through Grid Search to try and error, lack of effective automatic search parameters. In this study, we proposed a machine learning hyperparameter fine tuning service on dynamic cloud resource allocation system, which leverages several mainstream hyperparameter tuning methods such as flyperopt and Optunity. In the meanwhile, various tuning methods are measured and compared by example application in this work. Finally, we dedicated actual case - Heart Sounds, and then tested it. In order to verify that the system service can not only automate the task of tuning, but also break through the limitation of the number of adjustable parameters. Furthermore the proposed hyperparameter fine tune system makes optimization process more efficient.